Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models
Read the original on arXiv Machine Learning →The paper presents a framework called SNPE‑TTT for adapting pre‑trained neural posterior estimators, specifically conditional normalizing flows, to new inference tasks in agent‑based models (ABMs). It explores four adaptation strategies—full‑parameter adaptation, low‑rank adaptation, and two gradient‑based update spaces—and evaluates them on economic simulation models with both tractable and intractable likelihoods. Results show that the framework can improve posterior recovery with a limited number of target simulations, though performance varies by task and adaptation method.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.